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분할된 휴대 수하물 데이터셋에 대한 다중 Pipeline과 결합한 다중 Student-Teacher 프레임워크의 효과에 관한 연구
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- Issue Date
- 2022-11
- Citation
- 대한전자공학회 학술 대회 (추계) 2022, pp.2557-2561
- Publisher
- 대한전자공학회
- Language
- Korean
- Type
- Conference Paper
- Abstract
- In this paper, we investigate the student-teacher framework involving multiple mean teachers coupled with the multiple pipelines on the partitioned SIXRay-10 dataset. Firstly, SIXRay-10 dataset is partitioned and the multiple pipelines are applied with ResNet-50 backbone and Faster R-CNN detector to get the enhanced detection accuracy of 80.6 mAP0.5. Secondly, for each pipeline, a mean teacher model is defined as an exponential moving average of the student model parameters and the pipelines are dynamically aligned according to its bounding box regression loss to get 82.1 mAP0.5.
- KSP Keywords
- Bounding Box, Coupled with, Detection accuracy, Exponential Moving Average(EMA), Faster r-cnn, Model parameter, Student model, Student-teacher, Teacher Model